VentureBeat AIApr 12, 2026Apr 13, 2026, 04:00 JST

Data drift in ML security models creates dangerous vulnerabilities as attackers exploit outdated threat detection patterns

Data drift occurs when statistical properties of ML input data change over time, causing security models trained on historical data to become less accurate at detecting modern threats

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